A Selective Novel Peroxisome Proliferator-Activated Receptor (PPAR)-α Antagonist Induces Apoptosis and Inhibits Proliferation of CLL Cells In Vitro and In Vivo
Bibliographic record
Abstract
Tumor-specific metabolic changes can reveal new therapeutic targets. Our findings implicate a supporting role for fatty acid metabolism in chronic lymphocytic leukemia (CLL) cell survival. Peroxisome proliferator-activated receptor (PPAR)-α, a major transcriptional regulator of fatty acid oxidation, was recently shown to be upregulated in CLL. To evaluate PPARα as a potential therapeutic target, we developed a highly selective, potent small molecule antagonist of PPARα, NXT629. NXT629 inhibited agonist-induced transcription of PPARα-regulated genes, demonstrating target engagement in CLL cells. Furthermore, NXT629 induced apoptosis of CLL cells even in the presence of a protective microenvironment. To mimic the proliferative lymphoid compartment of CLL, we examined the activity of NXT629 on CLL cells that were stimulated to proliferate in vitro. NXT629 reduced the number of leukemia cells undergoing cell division. In addition, in two xenograft mouse models of CLL (one a model for nondividing and one for dividing CLL), NXT629 reduced the number of viable CLL cells in vivo. Overall, these results suggest that fatty acid metabolism promotes survival and proliferation of primary CLL cells and that inhibiting PPARα gene regulation could be a new therapeutic approach to treating CLL.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".